Enhancing exposure-based therapy from a translational research perspective

Combining an effective psychological treatment with conventional anxiolytic medication is typically not more effective than unimodal therapy for treating anxiety disorders. However, recent advances in the neuroscience of fear reduction have led to novel approaches for combining psychological therapy and pharmacological agents. Exposure-based treatments in humans partly rely on extinction to reduce the fear response in anxiety disorders. Animal studies have shown that D-cycloserine (DCS), a partial agonist at the glycine recognition site of the glutamatergic N-methyl-D-aspartate receptor facilitates extinction learning. Similarly, recent human trials have shown that DCS enhances fear reduction during exposure therapy of some anxiety disorders. This article discusses the biological and psychological mechanisms of extinction learning and the therapeutic value of DCS as an augmentation strategy for exposure therapy. Areas of future research will be identified.     

From grammatical number to exact numbers: early meanings of 'one', 'two', and 'three' in English, Russian, and Japanese

This study examined whether singular/plural marking in a language helps children learn the meanings of the words 'one,' 'two,' and 'three.' First, CHILDES data in English, Russian (which marks singular/plural), and Japanese (which does not) were compared for frequency, variability, and contexts of number-word use. Then young children in the USA, Russia, and Japan were tested on Counting and Give-N tasks. More English and Russian learners knew the meaning of each number word than Japanese learners, regardless of whether singular/plural cues appeared in the task itself (e.g., "Give two apples" vs. "Give two"). These results suggest that the learning of "one," "two" and "three" is supported by the conceptual framework of grammatical number, rather than that of integers.     

Beyond common features: the role of roles in determining similarity

Historically, accounts of object representation and perceived similarity have focused on intrinsic features. Although more recent accounts have explored how objects, scenes, and situations containing common relational structures come to be perceived as similar, less is known about how the perceived similarity of parts or objects embedded within these relational systems is affected. The current studies test the hypothesis that objects situated in common relational systems come to be perceived as more similar. Similarity increases most for objects playing the same role within a relation (e.g., predator), but also increases for objects playing different roles within the same relation (e.g., the predator or prey role in the hunts relation) regardless of whether the objects participate in the same instance of the relation. This pattern of results can be captured by extending existing models that extract meaning from text corpora so that they are sensitive to the verb-specific thematic roles that objects fill. Alternative explanations based on analogical and inferential processes are also considered, as well as the implications of the current findings to research in language processing, decision making, and category learning.     

Paired-associate learning, phoneme awareness, and learning to read

We report two studies examining the relations among three paired-associate learning (PAL) tasks (visual-visual, verbal-verbal, and visual-verbal), phoneme deletion, and single-word and nonword reading ability. Correlations between the PAL tasks and reading were strongest for the visual-verbal task. Path analyses showed that both phoneme deletion and visual-verbal PAL were unique predictors of a composite measure of single-word reading and of irregular word reading. However, for nonword reading, phoneme deletion was the only unique predictor (and visual-verbal PAL was not a significant predictor). These results are consistent with the view that learning visual (orthography) to phonological mappings is an important skill for developing word recognition skills in reading and that individual differences in this ability can be tapped experimentally by a PAL task.     

论学习迁移规律在中医教学过程中的地位与作用

学习迁移泛指一种学习对另一种学习的影响,一般指先前学习已获得的知识、技能、方法和态度对以后学习新的知识技能及研究解决问题的影响。通过学习迁移现象在中医教学过程中的不同表现,指出学习迁移规律是符合中医的教学规律的,有意识地应用学习迁移规律能够很好地促进中医教学,并就如何应用学习迁移规律改进中医教学提出了自己的看法。     

从“洪昭光热”探寻医学科普的出路、前景

洪昭光教授以及由其健康讲座所引发的"洪昭光热"引领了一种新的健康理念,不仅促进了医学健康知识的传播,更推动了医学科普走近大众、走进生活。如井喷一样的"洪昭光热"是有预热的,其主观成因和客观条件可以概括为时代背景、群众基础和社会价值。洪昭光教授找到了医学科普为大众喜闻乐见的根本——科学性、通俗性、实用性。"洪昭光热"让医学科普在引起社会民众的重视的同时,也为我们探寻医学科普的前景和出路提供了范本。     

IAT范式下自尊内隐性的再证明

该研究用IAT测量了被试的内隐自尊,并通过操纵被试在不同的认知负荷条件下进行外显自我评价来考察外显自我评价和内隐自尊之间的关系。得到的结论是,在低认知负荷条件下进行外显自我评价时,外显自我评价和内隐自尊没有表现出一致性;在高认知负荷条件下进行外显自我评价时,外显自我评价和内隐自尊表现出一致性。该研究在IAT范式下表明了内隐自尊和外显自尊的分离,内隐自尊具有无意识性和自动化的特点。     

两类数学学习困难小学儿童抑制控制水平的实验研究

为考察单纯型数学困难与混合型数学困难小学儿童的抑制控制水平及特点,使用Stroop色词命名测验和颜色匹配反转作业,对各30名的单纯型困难、混合型困难和对照组小学儿童的优势反应抑制能力进行测试、分析。结果发现:单纯型数学困难儿童抑制优势反应的能力显著低于对照组,但其对事物初次学习的能力与对照组相当;混合型数学困难儿童在对事物初次学习能力及对优势反应的抑制能力方面均显著低于对照组儿童,其中对事物的初次学习能力也显著低于单纯型数学困难儿童。     

Using Adult Learning Theory Concepts to Address Barriers to Cancer Genetic Risk Assessment in the African American Community

Utilization of cancer genetic risk assessment can be profoundly influenced by an individuals’ knowledge of risk assessment, attitudes regarding illness and healthcare, and affective reactions derived from social norms. Race and ethnicity play a powerful role in the development of an individual's attitudes and should be considered when attempting to understand a person's openness to cancer genetic risk assessment (Lannin et al., 1998). Until recently, however, cancer screening and prevention programs have been primarily based on data from studies conducted with the Caucasian population, yielding data that are not fully applicable to the African American community. In the last several years, research findings regarding African American's knowledge, attitudes, and feelings about genetic counseling and testing have grown (Matthews et al., 2000; Singer et al., 2004; Thompson et al., 2003). However, to the authors’ knowledge, these data have yet to be presented in a manner that both summarizes the barriers that African Americans have reported regarding cancer genetic risk assessment, while at the same time suggesting methods individual genetic counselors can utilize during community presentations to help address these barriers. This article will first summarize previous empirical findings regarding African Americans’ knowledge, attitudes, and feelings about cancer genetic risk assessment. The article will then apply adult learning theory to those findings to provide genetic counselors with practical, theory based techniques to apply toward community based educational programs with African American groups.     

A tutorial on kernel methods for categorization

The abilities to learn and to categorize are fundamental for cognitive systems, be it animals or machines, and therefore have attracted attention from engineers and psychologists alike. Modern machine learning methods and psychological models of categorization are remarkably similar, partly because these two fields share a common history in artificial neural networks and reinforcement learning. However, machine learning is now an independent and mature field that has moved beyond psychologically or neurally inspired algorithms towards providing foundations for a theory of learning that is rooted in statistics and functional analysis. Much of this research is potentially interesting for psychological theories of learning and categorization but also hardly accessible for psychologists. Here, we provide a tutorial introduction to a popular class of machine learning tools, called kernel methods. These methods are closely related to perceptrons, radial-basis-function neural networks and exemplar theories of categorization. Recent theoretical advances in machine learning are closely tied to the idea that the similarity of patterns can be encapsulated in a positive definite kernel. Such a positive definite kernel can define a reproducing kernel Hilbert space which allows one to use powerful tools from functional analysis for the analysis of learning algorithms. We give basic explanations of some key concepts—the so-called kernel trick, the representer theorem and regularization—which may open up the possibility that insights from machine learning can feed back into psychology.